TY - JOUR
T1 - Varying effects of tree cover on relationships between satellite-observed vegetation greenup date and spring temperature across Eurasian boreal forests
AU - Ding, Chao
AU - Meng, Yuanyuan
AU - Huang, Wenjiang
AU - Xie, Qiaoyun
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China ( 42201348 ), a funding from Chinese Academy of Sciences ( 183611KYSB20200080 ), and the SINO-EU Dragon 5 ( 57457 ). We thank the anonymous reviewers for their constructive comments on the manuscript.
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/11/15
Y1 - 2023/11/15
N2 - Earth observation satellites have facilitated the quantification of how vegetation phenology responds to climate warming on large scales. However, satellite image pixels may contain a mixture of multiple vegetation types or species with diverse phenological responses to climate variability. It is unclear how these mixed pixels affect the statistical relationships between satellite-derived vegetation phenology and climate factors. Here, we aim to investigate the impacts of percent tree cover (PTC), a measure of mixed pixel, on the statistical relationships between satellite-derived vegetation greenup date (GUD) and spring air temperature across Eurasian boreal forests at a 0.05° spatial resolution. We estimated GUD using Moderate Resolution Imaging Spectroradiometer (MODIS) time series data. The responses of GUD to interannual variation in spring temperature (April to May) during 2001–2020 were characterized by correlation coefficient (RTAM) and sensitivity (STAM). We then evaluated the local impacts of PTC on spatial variations in RTAM and STAM using partial correlation analysis through spatial moving windows. Our results indicate that, for most areas, forests with higher PTC were associated with stronger RTAM and STAM. Moreover, PTC had stronger local impacts on RTAM and STAM than mean annual temperature and temperature seasonality for 37.3% and 27.4% of the moving windows, respectively. These impacts were spatially varying and different among forest types. Specifically, deciduous broadleaf forests and deciduous needleleaf forests tend to have a higher proportion of these impacts compared to other forest types. Our findings demonstrate the nonnegligible effects of PTC on the statistical responses of GUD to temperature variability at coarse spatial resolution (0.05°) across Eurasian boreal forests.
AB - Earth observation satellites have facilitated the quantification of how vegetation phenology responds to climate warming on large scales. However, satellite image pixels may contain a mixture of multiple vegetation types or species with diverse phenological responses to climate variability. It is unclear how these mixed pixels affect the statistical relationships between satellite-derived vegetation phenology and climate factors. Here, we aim to investigate the impacts of percent tree cover (PTC), a measure of mixed pixel, on the statistical relationships between satellite-derived vegetation greenup date (GUD) and spring air temperature across Eurasian boreal forests at a 0.05° spatial resolution. We estimated GUD using Moderate Resolution Imaging Spectroradiometer (MODIS) time series data. The responses of GUD to interannual variation in spring temperature (April to May) during 2001–2020 were characterized by correlation coefficient (RTAM) and sensitivity (STAM). We then evaluated the local impacts of PTC on spatial variations in RTAM and STAM using partial correlation analysis through spatial moving windows. Our results indicate that, for most areas, forests with higher PTC were associated with stronger RTAM and STAM. Moreover, PTC had stronger local impacts on RTAM and STAM than mean annual temperature and temperature seasonality for 37.3% and 27.4% of the moving windows, respectively. These impacts were spatially varying and different among forest types. Specifically, deciduous broadleaf forests and deciduous needleleaf forests tend to have a higher proportion of these impacts compared to other forest types. Our findings demonstrate the nonnegligible effects of PTC on the statistical responses of GUD to temperature variability at coarse spatial resolution (0.05°) across Eurasian boreal forests.
KW - Climate change
KW - Eurasian boreal forests
KW - Mixed pixel
KW - Remote sensing
KW - Temperature sensitivity
KW - Tree cover
KW - Vegetation phenology
UR - http://www.scopus.com/inward/record.url?scp=85165535847&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2023.165650
DO - 10.1016/j.scitotenv.2023.165650
M3 - Article
C2 - 37474076
AN - SCOPUS:85165535847
SN - 0048-9697
VL - 899
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 165650
ER -